O ústavu

Život ústavu

Intranet

Financial Econometrics

Field characteristic

A research in the financial econometrics is
on the cutting edge of the current research. Financial markets have very
complex nature and cannot be easily understood using simple, tractable
models. Thus the financial markets are investigated from the various
perspectives.

The most important one, heterogeneous
agents approach, shifts the traditional paradigm of financial markets from
the fully rational agent to the agent with rationality constraints. Thus
traditional efficient market hypothesis can be abandoned and stock markets
can be viewed as a system of the interacting heterogeneous agents. The
idea of bounded rationality in the market participants' decisions is also
supported by the fractal features of the stock market prices.

The notion of multifractality
incorporates different investment horizons of the market p layers with
potentially various dynamics. Closely related to fractality is a research
on long-range dependence of volatility. A combination of these two
phenomena brings new approaches to financial econometrics, and new
theoretical and empirical results. Our research also includes the study of
stock market crashes, which have great impact on our society and are of a
great interest.

Finally, part of our research team
focuses also on the high-frequency data analysis. In recent years, this
research changed direction thanks to availability of high-frequency data
and so-called realized measures became a workhorse of financial
econometrics. While they have appealing asymptotic features, the
assumptions on zero microstructure noise and no jump presence in the data
are too restrictive. These observations cause a large bias to most of the
estimators. While inference under the noise and jumps in realized
variation theory is widely studied in recent contributions, realized
covariation theory still waits for its development. The covariation
between asset returns is crucial in risk management, portfolio
optimization and trading strategies as well as for option pricing. Wavelet
methods allow us to study volatility and correlations on various
investment horizons.